Abstract

Background: Technology is constantly evolving with respect to production agriculture. Precision farming
technologies have been increasingly recognized for their potential ability for improving agricultural
productivity, reducing production cost, and minimizing damage to the environment.
Methods: The combined use of different sensors and the following analysis of the recorded data allowed to define an
efficient technique for crop monitoring.
Results: We obtained the volume reconstruction of several plants and the NDVI mapping by exploiting
the proposed technique.
Conclusion: Crop monitoring and yield forecasting play a major role in the agricultural context. Future
work will be devoted to develop and use a customized index structure to increase the efficiency and to
integrate in our knowledge base some geo referential data.

1. Introduction

The use of innovative technologies summarized as Precision
Agriculture (PA) is a promising approach to optimize agricultural
production of crops. In crop production, precision agriculture
methodologies are applied for the site-specific application of fertilizer
or pesticides, automatic guidance of agricultural vehicles, product
traceability, on-farm research and management of production systems.
Precision agriculture can be described as information technology
applied to agriculture. Farmers have always known that certain parts of
a field produce differently than others, but until precision agriculture
came along, farmers lacked the technology to apply this knowledge.
The basis of precision agriculture is that it allows the study of fields on
a much finer resolution. This in turn provides opportunities for higher
yields and lower costs. Farm producers may have the opportunity to
optimize production outputs with the application of technologies of
precision agriculture.

This new farming management concept is based on observing,
measuring and responding to inter and intra-field variability in crops.
Crop variability typically has both a spatial and temporal component
which makes statistical/computational treatments quite involved. It is
only through the adoption of GPS (Global Positioning System) control
that this has become possible for farmers. GPS has given farmers the
ability to locate their precise position in a field which allows them
to measure and record geo-referenced information. This information
can then be used in the future to influence management decisions.

Thus, advances in machinery that include the ability to accumulate
detailed crop production data and advances in analytical tools to
distill these data into performance metrics are changing the way these
businesses are managed. For many years, farmers have benefited from
the use of yield monitoring data in making management decisions.
Since the late 1980s, technology for yield monitoring and accurate
positioning has been available with the expansion of application since
the early 1990s. The basis of precision agriculture is the opportunity
yield monitoring affords in the provision of spatially detailed
information in management when coupled with appropriate methods
and analysis. One could simply say this is essentially the application of
information technology to agriculture.

Consequently, taking into account the relevance of the applications
of the modern computer science technologies in the agricultural
context, we decided to develop a mobile unmanned terrestrial vehicle
(UTV) aimed at assisting the crop monitoring [1,2]. The remainder
of the paper is organized as follows: section 2 describes the most
relevant related works in the current literature in the context of the
agricultural technologies. Section 3 discusses the materials and the
methods used, while section 4 presents the experimental method.
Finally, some preliminary experimental results are shown in section
5 and conclusions with possible future work are provided in section 6.

2. Related work

In agriculture, knowledge of the characteristics of plants is
essential to perform an efficient and effective management of crops.
In recent years, the availability of affordable sensors and electronic
systems capable of facilitating the performance of intensive
measurements has gradually replaced traditional methods based on
manual measurements. Currently, there is hardly any relevant plant
characteristic without an associated sensory system based on the use
of electronics for its determination.

As a result, the accuracy of the measurements has drastically
increased; data acquisition has been eased, lightened and, in many
cases, automated. Thus, the traditional analysis of a reduced number
of manually-collected data has given way to the processing of files
with huge amounts of data resulting from the measurements provided
by the sensors and decision making in crop management can be
supported by information now available and impossible to have in the
past. Among the characteristics of crops, geometry deserves special
mention (canopy height, width and volume) as well as structural
parameters (leaf area index, canopy porosity and permeability and
wood structure) due to their great influence on the behavior of plants
interacting with solar radiation, water a d nutrients at their disposal
Lee and Ehsani [3] as well as on the knowledge and prediction of the
vigor and quality of the produced crop. These parameters also have
a key role in assessing the efficiency and effectiveness of the main
operations performed in the orchards, such as the application of
inputs (fertilizers, irrigation and plant protection products against
pests and diseases), pruning and harvesting [4,5]. Several studies
have shown the existence of a relationship between the geometrical
parameters of a crop and yield. Among the geometric parameters of
plants, canopy volume has a special significance because it combines,
in a single variable, the width, the height, the geometric shape and the
structure of trees [6]. For this reason, its determination in a reliable,
systematic and affordable way, both in cost and time, is a priority in
the present and near future of Precision Agriculture/ Fructiculture
defined as the one that takes full advantage of the ICT (Information
and Communications Technology) systems, geostatistics and decision
making support systems. Usually, precise measurement of the volume
of a canopy requires of costly man-made measurements on the
plants with the corresponding time and economical cost. However,
several sensor-based approaches have been published in the scientific
literature dealing with the problem of estimating the canopy volume.
The techniques used to determine the canopy volumes are based either
on the use of electromagnetic radiation, mainly in the spectrum range
from the ultraviolet to the infrared, including the visible, or on the use
of ultrasonic waves. The most widespread systems in the first group
are those based on the use of digital photography, photogrammetric,
and stereoscopy techniques as well as LiDAR (Light Detection and
Ranging) sensors [5]. A LiDAR sensor estimates the distance apart
of the object of interest, using in some technologies the Time of
Flight (ToF) principle [7]. In practice most used LiDAR scanners
perform sweeps in a plane (2D) or in the space (3D) by modifying
the direction at which the laser beam is emitted. A very common
configuration in agricultural research applications is what is known as
mobile terrestrial laser scanner (MTLS), a 2D LiDAR sensor mounted
on a vehicle moving along the alley-ways between rows of trees in an
orchard in order to obtain the scanning of the entire crop in 3D, [8].
This operation mode usually requires a high precision GNSS receiver
to know the spatial coordinates of the LiDAR sensor at all times.

In Keightley and Bawden [9] a LiDAR sensor mounted on a
ground tripod is used for 3D volumetric modeling of a grapevine.
The system does not consider position errors and the validations were
performed under laboratory conditions. In Bucksch and Fleck [10]
and in Raumonen et al. [11] a ground fixed LiDAR sensor is used
to 3D model the tree skeletons, based on a graph splitting procedure
to extract branches from the cloud of points. Although the system
efficiently extracts the skeleton patterns from several trees, it does not
offer a real time solution and its robustness to leaves density is not
provided in the research. In the same line, Cote et al. [12], explores
and tests the use of LiDAR scanners in tree modeling. In addition,
Moorthy et al. [13] used 3D LiDAR to measure structural and
biophysical information of individual trees. Fieber et al. [14] used a
LiDAR to classify ground, trees and oranges using only the reflected
waveforms from the LiDAR, avoiding the need of using geometric
information. Although efficient, the proposal was not tested for real
time implementations but for batch processing only. In addition, no
information is provided regarding shapes or sizes of the agricultural
features. In Walklate et al. [15] a LiDAR sensor and a GPS receiver
are mounted on a same chassis for 3D reconstruction of orchards.
No information is provided regarding the geometric processing.
Instead, the research is focused on using the 3D information for
spray management. The performance of the previous methods relies
on the precision of the GPS (see Auat Cheein and Guivant, [16].
Mendez et al. [17] used a LiDAR for skeleton reconstruction of a
grove and for vegetative measures.

On the other hand, Jaeger-Hansen et al. [18] uses a similar hardware
and provides a first estimate of the treetop surface using ellipses and
minimum square fitting techniques.

In addition, in Rosell et al. [19], a first study in 3D orchard
reconstruction is presented, in which a LiDAR and a differential GPS
are used for mapping the environment. Moreover, optical crop-sensing
technology has been around for several years [20,21]. Optical cropsensing
systems use light sensors to analyze in-season plant health.
Healthy plants absorb more red lights, and less healthy plants take
in less. Optical crop-sensing systems are currently available mostly
through Trimble with Green Seeker and AgLeader with OptRx. Both
systems measure crop status and variably apply the crop’s nitrogen
requirements in anhydrous, dry or liquid form. While both optical
crop-sensing systems generally work the same, there are differences.
Green Seeker calculates Normalized Difference Vegetative Index
(NDVI), a way to measure plant health [22]. The NDVI is an indicator
of biomass living plant tissue and is combined with known growingdegree
days to project yield potential [23,24].

To help determine the correct nitrogen recommendations and
application rates, Green Seeker calibrates using a nitrogenrich strip
which is really a control base. The strip is used to establish a midseason
determination of additional nitrogen requirements. The strip
must be present in each field and should be 300 to 500 feet long, and
in a representative area of the field. It should not be in the upland or
bottom area, but should have different soil types or topography.

OptRx utilizes a virtual reference strip instead of a nitrogen rich
strip for making nitrogen recommendations [25]. Much like Green
Seeker, the OptRx system assigns a vegetation index value based on
plant biomass and nitrogen content. Thus, the OptRx crop sensor uses
a single algorithm to control application.

3. Materials and Methods

We have developed a crop monitoring mobile unmanned terrestrial
vehicle based on a combined use of optical sensors aimed to assist our
preliminary tests.

3.1 The mobile lab

The main materials used to develop the overall system are a set of
co-operating sensors. More in details, we used 2 Lidar sensors and
3 OptRx crop sensors, which are described in SubSections III-B and
III-D. Moreover, a sonar sensor, in order to have a better indication
of the position of the mobile lab, in terms of distance from a movable
target (e.g., a vertical panel) and a RTK GPS are installed on the
system.

The real time kinematic GPS (RTK GPS), provides GPS position
accuracy to within 1 centimeter. RTK GPS requires a separate base
station located within approximately 5 miles of the mobile GPS units.
Thus, the RTK base station is a known location equipped with a GPS
unit. The base station GPS location is corrected to its known location,
and the correction factor is transmitted to the mobile GPS units by
FM radio signals. The accuracy of RTK GPS results from the close
proximity of the base correction station. However, the data acquisition
phase is essential for our whole process. Thus, the data captured from
the sensors in our system are in the order of millions (for instance 6.5
GBs when considering a one-hectare field), we plan to define and
exploit in the future some specific indices in order to significantly
improve the data analysis efficiency. For example, we can use
a customized implementation of the tMAGIC [26] (Temporal
Multiactivity Graph Index Creation) index, that first stores into a
huge log (called temporal multiactivity graph) the multiple activities
that need to be concurrently monitored and then defines an efficient
algorithm to find the activity that best matches a sequence of
observations. This index has been already efficiently exploited by
other relevant works, such as [27-30].

We also plan to carry out two different types of experiments:

A very accurate but less representative reconstruction just of
a small subset of the overall data set. We will be working at a
very low speed on small plots (5 m). The total disk consumption
would be around 3.25 MBs.

A less accurate but very representative reconstruction of the
overall plot (one hectare). We will be using a low sampling
frequency and working at very high speeds (4km/h). The total
disk consumption would be around 6.5 GBs.

3.2 Lidar Sensor

In this experimental work the two LIDAR scanners used were
a general-purpose SICK LMS-111 model (Sick 1, Du sseldorf,
Germany) (Figure 1), with a range accuracy of 30 mm, a selectable
angular resolution of 0.5 degrees and a scanning angle of 270 degree.
Moreover, the LMS-111 has a standard RS232 serial port for data
transfer and an ethernet port.

Figure 1:
Scheme of the overall system.

The software tools used along with the two LIDAR sensors are SOPAS
ET 1.0.4, to complete the customized configuration of the LIDAR
device, Hercules Setup Utility 3.2.8, to manage the communication
with the sensor and the data transmission, and Matlab, to make a
detailed analysis on the data obtained during the previous steps.
When the laser beam is intercepted by the surface of vegetation, the
sensor determines from the reflected signal the angular position q and
the radial distance r between the target interception point and LIDAR
position. The sensor continuously measures distances at the selected
angular resolution.

This information represented a vertical outline (or slice) of the tree
for the current position of the LIDAR. When moved along the rows,
the LIDAR scanner supplied a cluster or cloud of plant interception
points in polar coordinates (r,q). Although the LMS-111 LIDAR is
a 2D laser scanner, the displacement of the laser sensor along the
direction (Z) parallel to the row of trees at a known constant speed, and
the use of software allowed a 3D graphic representation of the cloud of
plants interception points to be developed, such that a nondestructive
record of the tree-row structure of the crop was obtained. Once the
3D cloud of points was obtained, efforts were focused on obtaining
the geometrical and structural parameters of the tree and bush crops
under examination.

3.3 The developed LIDAR/NDVI algorithm

Considering the following input/output parameters:

X1: higher-level lidar coordinate.

X2: lower-level lidar coordinate.

tol: tolerance defined by the user.

X: X coordinate value,

The developed LIDAR/NDVI algorithm is defined as follows
(Algorithm 1).

Algorithm 1 LIDAR/NDVI algorithm

Input: LIDAR data, NDVI data, tol

Output: X

1: Extraction of the cylindrical coordinates.

2: Conversion into cartesian coordinates.

3: Mesh generation along Z and Y of size d (defined by the user
(d=0.01 m)) and interpolation of the Lidar data for the X coordinate.

4: Merging the both side Lidar data.

5: Merging data algorithm on the base of tol.

6: X = X coordinate evaluation(X1;X2; tol) (see Algorithm 2).

7: Mesh generation along Z and Y with the same size d as Lidar and
NDVI data interpolation.

3.4 OptRx Crop Sensor

Available from AgLeader, the three OptRx Crop Sensors used in
our work2 are able to record and measure real time information.

Algorithm 2 X coordinate evaluation

Input:X1, X2, tol

Output:X

1: if |X1- X2| < tolthen

2: X = (X1 + X2) / 2

3: else

4: ifX2> X1then

5: X = X2

6: else

7: X = X1

8: Return X

OptRx crop sensors work by shining light on the crop canopy and
reading the light reflected back to determine the crop health, also
known as Vegetative Index. Moreover, the Vegetative Index (VI)
aquired are NDVI (Normalized Difference Vegetation Index) and
NDRE (Normalized Difference Red Edge Index) with a scanning
angle of 45 - 10 degrees and a measurement range of 0.25-2 m. The
normalized difference vegetation index (NDVI) is historically one of
the first VIs. It is a normalized ratio of the NIR (near infrared) and
red bands [31].

NDV I = (NIR - Red)=(NIR + Red)
(1)

Theoretically, NDVI values range from +1.0 to -1.0. Areas of barren
rock, sand, or snow usually show very low NDVI values (for example,
0.1 or less). Sparse vegetation such as shrubs and grasslands or
senescing crops may result in moderate NDVI values (approximately
0.2 to 0.5). High NDVI values (approximately 0.6 to 0.9) correspond
to dense vegetation such as that found in temperate and tropical
forests or crops at their peak growth stage.

Moreover, the Normalized Difference Red Edge [32] uses the
NDVI form but substitutes its bands by a red edge band at 720 nm
and a reference band from the NIR plateau at 790 nm. Since OptRx
uses its own light sensing technology, there is no dependence upon
ambient light or angle of the sun [33]. OptRx can be used day or night
when optimum conditions are available. Data collected is logged
and mapped into a device, which automatically records application
activities, including applied areas, product volume, and more. After
that, using the proprietary AgLeader Software SMS basic, the different
information can be easily downloaded into AgLeader SMS software
for analysis. Consequently, using this information and also managing
it, we were able to use the 3D-Field Software to process the optical
measurement in order to obtain the NDVI maps which immediately
show differences in vegetative development among plant groups.

3.5 The overall system

Unmanned Terrestrial Vehicle (UTV)

two LIDAR sensors

tree NDVI crop sensors

one Sonar range finder

Measurements were made using the UTV which supports our six
sensors and traverses the experimental crop in direction Z, parallel
to the row at a known and costant speed; the crop used for such
experiments are described in the following section. More specifically,
the LIDAR sensors were placed on our UTV at two different heights
(0.544 and 1.531 m), vertically aligned and scanning the same targets;
furthermore, the OptRX sensors were also put on the UTV at three
different heights (0.59, 1.088, 1.587 m), still vertically alligned and
scanning the same targets (Figure 1 and 2).

Figure 2:
Picture of the overall system.

Moreover, the progress speed has been estimated through sonar
managed by the Arduino board that was mounted on the overall
system as well. The sensor measures distances, that are considered
as instantaneous as well as the sample speed. The average constant
progress speed has been chosen as reference speed value. This speed
was 0.3-0.5 m/s for both sensors.

4. Experimental Method

The Unmanned Terrestrial Vehicle (UTV) developed was used to
characterize some common trees (Figure 3). The species analyzed are
the following:

Cupressocyparis Leylandii Spiral, height 160 cm

Cupressocyparis Leylandii Ball, height 140 cm

Cupressocyparis Leylandii Pon Pon, height 160 cm

Juniperus virginiana, height 240 cm

Pachira, height 110 cm

Ficus benjamina, height 160 cm

Figure 3:
Scheme of field tests.

The trees were aligned in a chain from left to the right, in the order
listed above. Then, a fake plant whose layout is obviously well-known
is added to the top (i.e., left) of this plant line. Furthermore, some
reference panels [34] were placed as shown in Figure 3. Thus, an
experimental activity aimed at acquiring data in different scenarios
related to a decreasing distance between the plants - from a maximum
of 1 meter to a minimum close to 0 - was performed.

5. Results and Discussion

Following the experimental method previously explained, we
obtained some very promising results. The main innovations of this
case study are the combined use of Lidar and NDVI sensors to get real
time information about both the geometric shapes and the vegetative
states of the plants, and the canapy reconstruction of our plant line,
using the path shown in Figure 3.

For the first of the reasons listed above, the data obtained with the
Lidar and NDVI elaborations have been processed using Matlab,
in order to discover the correspondences of these point clouds and
their correct over lapping. The scan performed by the LIDAR sensors
(Figure 4) meshed with the OptRx ones (Figure 5) showed some really
interesting results. From the exhibited diagrams, we can notice how
the NDVI maps reconstruct the vegetative state of the plant line of
interest.

Figure 4:
Left and right Lidar scan.

Figure 5:
Left and right LIDAR/NDVI mesh.

The LIDAR + NDVI diagram in (Figure 6) was plotted in order
to discover the X coordinate of the plant midpoint. The obtained X
coordinate of the plant midpoint is x0 = 1.85 m. Then, we exploited
such a value to compute the vegetative thickness (d) as follows:

The vegetative thickness on the X axis and the Z coordinate on
the Y axis

The NDVI index on the X axis and the Z coordinate on the Y
axis both for the left and for the right sides, as depicted in the
following diagrams Figure 7.

Figure 7:
Plot of the vegetative thicknes along the z coordinate for y=1
and y = 0.6.

On the other hand, the following diagrams Figure 8 & 9 were plotted
still taking as reference the same height values, but considering some
20 cm slots along the Y axis, then computing the average of both the
thickness and NDVI. Moreover, in the following diagrams (Figure 10,
11), we can notice a change in the NDVI index trend in correspondence
to a high thickness value. Differently from the previous diagrams, in
the following ones we used some 10 cm slots along the Y axis.

Figure 8:
Plant slot segmentation. For each Z coordinates, the average of
the thickness and NDVI is computed for the specific slot of interest.

Figure 9:
Plot of the vegetative thickness and NDVI vegetation index
along the z coordinate for y = 1.0 and y = 0.6.

Another area where the NDVI index is low can be found in
correspondence to the sphere shaped plant trunk, obviously because
there is no vegetation.

Furthermore, a 40 cm slot as a whole was used, 20 with reference
to the height of interest.

Eventually, the diagrams below (Figure 12) were plotted to compare
the curve trends when fixing the height value (y) to 0.6 m and using a
different slot size; at left 20, while at right 10. In the right one, we can
notice a more marked change in the NDVI trend.

For the second one, the tractor completed a path while scanning
both the sides of the plant line analyzed. From this scan we derived
the following diagram (Figure 6) showing an excellent volume
reconstruction of the plant line in both the scanned sides. As a matter
of fact, as depicted in Figure 13, the reconstructed plants are very
similar to their real geometric shapes.

Figure 13:
Picture of the trees analyzed.

Figure 14:
Legend of the novel diagnostic algorithm.

Eventually, the two reconstructions have been meshed in order to
obtain the overall line volume reconstruction. In order to improve the
visual representation of the vigour map, we have implemented a novel
diagnostic algorithm based on the matrix of Figure 14. The matrix
rows discretize the vegetation thickness in three different ranges:

t ≺ 0.1m

0.1 ≺ t ≺ 0.2m

t ≻ 0.2m

While the matrix columns discretize the NDVI index in three
different ranges:

NDVI ≺ 0:6

0.6 ≺ NDVI ≺0:8

NDVI ≺ 0:8

As a result, the black element of the matrix represents an area without
vegetation. The yellow elements describe a critical situation, while the
red ones correspond to a very critical vegetation area. Eventually, the
green elements outline the healthy vegetation. According to Figure 14,
this novel diagnostic algorithm highlights a better different vegetation
health status in comparison with the NDVI representation of Figure
10. Plants B and E are healthy. Plant D has a vigour region, while has
also some unhealthy regions where foliage is sparse in the bottom part
of the trunk. Plant C has a critical region because the NDVI sensor
captured the woody zone in the middle of the sphere. Finally, plant
A is overall healthy, but it shows a much stressed area (the unhealthy
leaf marked in red).

The novel diagnosis algorithm showed an excellent potential in this
preliminary investigation stage. In order to correctly illustrate the
health condition of the plants, a refinement of this algorithm will be
carried out in the future experiments to find out the optimal range of
vegetation thickness and the NDVI vegetation index to be applied to
the diagnosis matrix.

Finally, the LIDAR (Light Detection and Ranging) sensor
technology installed on the unmanned terrestrial vehicle (UTV) is
equipped with a rotating mirror mechanism, which deflects the laser
beam emitted. With such a mechanism the LIDAR allows for time
measurements not just of a point, but of a 2D slice of the environment.
In case of bad weather conditions (rain or snow) a laser pulse can be
reflected by a raindrop or a snowflake preventing from measuring the
object of interest.

In order to improve the LIDAR performance in bad weather
conditions, a multi-echo technology was introduced. When a laser
pulse is emitted, the energy propagates through the environment in
a cone shape, as not all of it will typically be reflected by a rain drop
or snow flake. However, it must be keep in mind that the mobile lab
was conceived to detect crops under normal weather conditions, in
order to avoid any type of undesired interference with the measuring
system. Furthermore, the NDVI sensors can operate autonomously,
emitting pulse light.

6. Conclusion

Recent trends in global agriculture prices have brought a new
scenario for agricultural policies worldwide. Increased world demand
for agricultural products combined with interannual fluctuations of
global production mostly caused by climate variability have been an
important cause for price volatility in agricultural markets, and social
unrest in many parts of the world. In this context, crop monitoring
and yield forecasting play a major role in anticipating supply
anomalies, thus allowing well-informed timely policy action and
market adjustment, preventing food crises and market disruptions,
reducing market speculation, and contributing to overall increased
food security.

Moreover, we plan to define and exploit in the future some specific
indices in order to significantly improve the data analysis efficiency,
potentially combined with the management of multimedia data
depicting, for instance, the plant evolution over time [35].

Future work will exploit the NDVI index, calculated from reflectances
in the red and near-infrared (NIR) portions of the spectrum, rather
than other indices such as EVI. The EVI index incorporates reflectance
in the blue portion of the spectrum in addition to the red and NIR.
Moreover, one of the biggest current limitations to implement EVI is
that it needs a blue band to be calculated. Not only does this limit the
sensors that EVI can be applied to (e.g., ASTER has no blue band),
but the blue band typically has a low signal-to-noise ratio. In addition,
further future experiments will be devoted to discover the optimal slot
sizes on which to average the thickness and the NDVI index, in order
to obtain a better representation and an automated statistical method
will be defined in the next future to both identify best predictors and
indices.

The solution here developed clearly focuses on orchard applications,
as it applies proximal sensing detections able to provide lateralviews
of the canopy, instead of top-views typically provided by
conventional remote sensing surveys carried out by flying vectors.
Future improvements are still expected in merit to aspects such as:
the capability to estimate the thinning intensity according to the
bloom charge, early diagnosis of diseases, and detection of nutritional
stresses.

However, we also plan to use our mobile lab in a wide range of
agricultural contexts, including arable crops. To this aim, some
minor modifications will be provided to the sensor supports in order
to be able to get typical top-views to acquire information on the
physiological state of the herbaceous crops.

Acknowledgments

The research leading to these results has been supported by the
Monalisa project, which was funded by the Autonomous Province of
Bozen-Bolzano.

References

Bietresato M, Vidoni R, Gasparetto A, Mazzetto F (2015) Design and
first tests of a vision system on a tele-operated vehicle for monitoring the
canopy vigour status in orchards. Near Surface Geoscience.
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